Mask topology optimization method and system for surface plasmon near-field photolithography

Patent metadata

Field	Value
Publication number	US-12092960-B2
Application number	US-202118575532-A
Country	US
Kind code	B2
Filing date	Oct 14, 2021
Priority date	Oct 14, 2021
Publication date	Sep 17, 2024
Grant date	Sep 17, 2024

Abstract

A mask topology optimization method for surface plasmon near-field photolithography, including: acquiring first mask data and performing fuzzy processing and projection processing on same to obtain second mask data; performing forward calculation according to the second mask data and a preset surface plasmon near-field photolithography condition to obtain imaging data and forward field data; calculating an imaging error between the imaging data and expected imaging data; performing adjoint calculation on the second mask data to obtain adjoint field data; calculating a gradient matrix of the imaging error relative to the first mask data according to the forward field data and the adjoint field data; and updating the first mask data according to the gradient matrix, repeating the steps for iteration calculation until the optimized mask data is obtained, and outputting a final mask pattern.

First claim

What is claimed is:

1. A mask topology optimization method for surface plasmon near-field photolithography, comprising steps of:
acquiring a first mask data and performing fuzzy processing and projection processing on the same to obtain a second mask data;
performing forward calculation according to the second mask data and a preset surface plasmon near-field photolithography condition to obtain an imaging data and a forward field data;
calculating an imaging error between the imaging data and an expected imaging data;
performing adjoint calculation on the second mask data to obtain an adjoint field data;
calculating a gradient matrix of the imaging error relative to the first mask data according to the forward field data and the adjoint field data; and
updating the first mask data according to the gradient matrix, repeating the above steps for iteration calculation until the optimized mask data is obtained, and outputting a final mask pattern.

2. The mask topology optimization method for surface plasmon near-field photolithography according to claim 1 , wherein the step of calculating an imaging error between the imaging data and expected imaging data comprises:
determining whether the imaging error is less than a threshold or whether a current accumulated iteration number is greater than a set value, wherein if yes, the current mask data is output as the optimized mask data; otherwise, the iteration calculation is continued.

3. The mask topology optimization method for surface plasmon near-field photolithography according to claim 1 , wherein the forward calculation comprises:
simulating by using an exposure light source excitation source; and
solving by using a finite-difference time-domain algorithm to obtain the imaging data and the forward field data.

4. The mask topology optimization method for surface plasmon near-field photolithography according to claim 1 , wherein the adjoint calculation comprises:
simulating by using an adjoint excitation source; and
solving by using a finite-difference time-domain algorithm to obtain the adjoint field data.

5. The mask topology optimization method for surface plasmon near-field photolithography according to claim 1 , wherein the step of acquiring a first mask data comprises:
acquiring an initial mask pattern based on an expected imaging pattern;
performing pixelation processing on the initial mask pattern to obtain an initial mask data and an expected imaging data; and
taking the initial mask data or an updated mask data as the first mask data to perform the fuzzy processing and the projection processing.

6. The mask topology optimization method for surface plasmon near-field photolithography according to claim 1 , wherein the fuzzy processing comprises:
performing the fuzzy processing by using the following formula:

ρ
~

(
r
)

=

∑

r
j

⁢

1
α

⁢

ρ
⁡
(
r
)

⁢

B
⁡
(

r
,

r
j

)

wherein α=Σ r j B(r, r j ), B(r, r j )=max {0, R filt −∥r−r j ∥}, R filt is a fuzzy radius, ρ(r) is the initial mask data or the updated mask data, i.e., the first mask data, {tilde over (ρ)}(r) is the mask data after the fuzzy processing, r is coordinate of any point in the current mask data, and Σ r j represents the summation at the r coordinate by traversing all coordinate points r j in the current mask data.

7. The mask topology optimization method for surface plasmon near-field photolithography according to claim 1 , wherein the projection processing comprises:
performing binarization projection processing by using the following formula:

ρ
_

(
r
)

=

{

η
⁢

e

β
⁡
(

η
-

ρ
~

(
r
)

)

η

-

(

η
-

ρ
~

(
r
)

)

⁢

e

-
β

Assignees

• Inst Optics & Electronics Cas

Inventors

• Luo Xiangang
• Xu Mingfeng
• Pu Mingbo
• Sang Di
• Ma Xiaoliang
• Li Xiong
• Gao Ping
• Zhao Zeyu

Classifications

• G03F7/705 Primary

  Modelling or simulating from physical phenomena up to complete wafer processes or whole workflow in wafer productions · CPC title

• G03F1/36

  Masks having proximity correction features; Preparation thereof, e.g. optical proximity correction [OPC] design processes · CPC title

• G03F1/50

  Mask blanks not covered by G03F1/20 - G03F1/34; Preparation thereof · CPC title

• G03F7/70433 Primary

  Layout for increasing efficiency or for compensating imaging errors, e.g. layout of exposure fields for reducing focus errors; Use of mask features for increasing efficiency or for compensating imaging errors · CPC title

• G06F30/392

  Floor-planning or layout, e.g. partitioning or placement · CPC title